Advancements in deep learning techniques have enabled end-to-end learning of rich representations of images for supervised learning. For the purposes of clustering, however, such features learned via supervised learning can not be obtained due to lack of availability of labels. Therefore, supervised learning approaches fall short of providing a solution. Self-supervised learning addresses the issue of learning representations without labeled data. Self-supervised learning is a sub-field of unsupervised learning in which the main goal is to learn general purpose representations by exploiting user-defined tasks (pretext tasks) [1]}, [2]}, [3]}, [2]}, [5]}, [6]}, [7]}.
These representation learning algorithms were shown to achieve good results when evaluated using linear evaluation protocol, semi-supervised training on ImageNet, or transfer to downstream tasks. A straight forward solution to the clustering problem is to use the features learned using self-supervised learning, and apply an out-of-the-box clustering algorithm (such as k-means) to compute clusterings of the data. However, the performance of these features for clustering (using an out-of-the-box clustering algorithm) is not known, and as seen in our results these features may be improved for clustering.
